Implement QueryResult type.

src/vectorcode/chunking.py

@@ -25,21 +25,36 @@ class Chunk:
     """
 
     text: str
-    start: Point
-    end: Point
+    start: Point | None = None
+    end: Point | None = None
+    path: str | None = None
+    id: str | None = None
 
     def __str__(self):
         return self.text
 
+    def __hash__(self) -> int:
+        return hash(f"VectorCodeChunk_{self.path}({self.start}:{self.end}@{self.text})")
+
     def export_dict(self):
+        d: dict[str, str | dict[str, int]] = {"text": self.text}
         if self.start is not None:
-            return {
-                "text": self.text,
-                "start": {"row": self.start.row, "column": self.start.column},
-                "end": {"row": self.end.row, "column": self.end.column},
-            }
-        else:
-            return {"text": self.text}
+            d.update(
+                {
+                    "start": {"row": self.start.row, "column": self.start.column},
+                }
+            )
+        if self.end is not None:
+            d.update(
+                {
+                    "end": {"row": self.end.row, "column": self.end.column},
+                }
+            )
+        if self.path:
+            d["path"] = self.path
+        if self.id:
+            d["chunk_id"] = self.id
+        return d
 
 
 @dataclass
@@ -129,7 +144,7 @@ def chunk(
     ) -> Generator[Chunk, None, None]:
         logger.info("Started chunking %s using FileChunker.", data.name)
         lines = data.readlines()
-        if len(lines) == 0:
+        if len(lines) == 0:  # pragma: nocover
             return
         if (
             self.config.chunk_size < 0

src/vectorcode/mcp_main.py

@@ -3,6 +3,7 @@
 import logging
 import os
 import sys
+import traceback
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Optional, cast
@@ -160,14 +161,17 @@ async def vectorise_files(paths: list[str], project_root: str) -> dict[str, int]
             await remove_orphanes(collection, collection_lock, stats, stats_lock)
 
         return stats.to_dict()
-    except Exception as e:
-        logger.error("Failed to access collection at %s", project_root)
-        raise McpError(
-            ErrorData(
-                code=1,
-                message=f"{e.__class__.__name__}: Failed to create the collection at {project_root}.",
-            )
-        )
+    except Exception as e:  # pragma: nocover
+        if isinstance(e, McpError):
+            logger.error("Failed to access collection at %s", project_root)
+            raise
+        else:
+            raise McpError(
+                ErrorData(
+                    code=1,
+                    message="\n".join(traceback.format_exception(e)),
+                )
+            ) from e
 
 
 async def query_tool(
@@ -211,24 +215,28 @@ async def query_tool(
                 configs=query_config,
             )
             results: list[str] = []
-            for path in result_paths:
-                if os.path.isfile(path):
-                    with open(path) as fin:
-                        rel_path = os.path.relpath(path, config.project_root)
-                        results.append(
-                            f"<path>{rel_path}</path>\n<content>{fin.read()}</content>",
-                        )
+            for result in result_paths:
+                if isinstance(result, str):
+                    if os.path.isfile(result):
+                        with open(result) as fin:
+                            rel_path = os.path.relpath(result, config.project_root)
+                            results.append(
+                                f"<path>{rel_path}</path>\n<content>{fin.read()}</content>",
+                            )
             logger.info("Retrieved the following files: %s", result_paths)
             return results
 
-    except Exception as e:
-        logger.error("Failed to access collection at %s", project_root)
-        raise McpError(
-            ErrorData(
-                code=1,
-                message=f"{e.__class__.__name__}: Failed to access the collection at {project_root}. Use `list_collections` tool to get a list of valid paths for this field.",
-            )
-        )
+    except Exception as e:  # pragma: nocover
+        if isinstance(e, McpError):
+            logger.error("Failed to access collection at %s", project_root)
+            raise
+        else:
+            raise McpError(
+                ErrorData(
+                    code=1,
+                    message="\n".join(traceback.format_exception(e)),
+                )
+            ) from e
 
 
 async def ls_files(project_root: str) -> list[str]:

src/vectorcode/subcommands/query/__init__.py

@@ -3,12 +3,13 @@
 import os
 from typing import Any, cast
 
-from chromadb import GetResult, Where
+from chromadb import Where
 from chromadb.api.models.AsyncCollection import AsyncCollection
-from chromadb.api.types import IncludeEnum
+from chromadb.api.types import IncludeEnum, QueryResult
 from chromadb.errors import InvalidCollectionException, InvalidDimensionException
+from tree_sitter import Point
 
-from vectorcode.chunking import StringChunker
+from vectorcode.chunking import Chunk, StringChunker
 from vectorcode.cli_utils import (
     Config,
     QueryInclude,
@@ -22,6 +23,7 @@
     get_embedding_function,
     verify_ef,
 )
+from vectorcode.subcommands.query import types as vectorcode_types
 from vectorcode.subcommands.query.reranker import (
     RerankerError,
     get_reranker,
@@ -30,14 +32,49 @@
 logger = logging.getLogger(name=__name__)
 
 
+def convert_query_results(
+    chroma_result: QueryResult, queries: list[str]
+) -> list[vectorcode_types.QueryResult]:
+    """Convert chromadb query result to in-house query results"""
+    assert chroma_result["documents"] is not None
+    assert chroma_result["distances"] is not None
+    assert chroma_result["metadatas"] is not None
+    assert chroma_result["ids"] is not None
+
+    chroma_results_list: list[vectorcode_types.QueryResult] = []
+    for q_i in range(len(queries)):
+        q = queries[q_i]
+        documents = chroma_result["documents"][q_i]
+        distances = chroma_result["distances"][q_i]
+        metadatas = chroma_result["metadatas"][q_i]
+        ids = chroma_result["ids"][q_i]
+        for doc, dist, meta, _id in zip(documents, distances, metadatas, ids):
+            chunk = Chunk(text=doc, id=_id)
+            if meta.get("start"):
+                chunk.start = Point(int(meta.get("start", 0)), 0)
+            if meta.get("end"):
+                chunk.end = Point(int(meta.get("end", 0)), 0)
+            if meta.get("path"):
+                chunk.path = str(meta["path"])
+            chroma_results_list.append(
+                vectorcode_types.QueryResult(
+                    chunk=chunk,
+                    path=str(meta.get("path", "")),
+                    query=(q,),
+                    scores=(-dist,),
+                )
+            )
+    return chroma_results_list
+
+
 async def get_query_result_files(
     collection: AsyncCollection, configs: Config
-) -> list[str]:
+) -> list[str | Chunk]:
     query_chunks = []
-    if configs.query:
-        chunker = StringChunker(configs)
-        for q in configs.query:
-            query_chunks.extend(str(i) for i in chunker.chunk(q))
+    assert configs.query, "Query messages cannot be empty."
+    chunker = StringChunker(configs)
+    for q in configs.query:
+        query_chunks.extend(str(i) for i in chunker.chunk(q))
 
     configs.query_exclude = [
         expand_path(i, True)
@@ -70,7 +107,7 @@ async def get_query_result_files(
         query_embeddings = get_embedding_function(configs)(query_chunks)
         if isinstance(configs.embedding_dims, int) and configs.embedding_dims > 0:
             query_embeddings = [e[: configs.embedding_dims] for e in query_embeddings]
-        results = await collection.query(
+        chroma_query_results: QueryResult = await collection.query(
             query_embeddings=query_embeddings,
             n_results=num_query,
             include=[
@@ -85,69 +122,51 @@ async def get_query_result_files(
         return []
 
     reranker = get_reranker(configs)
-    return await reranker.rerank(results)
+    return await reranker.rerank(
+        convert_query_results(chroma_query_results, configs.query)
+    )
 
 
 async def build_query_results(
     collection: AsyncCollection, configs: Config
 ) -> list[dict[str, str | int]]:
-    structured_result = []
-    for identifier in await get_query_result_files(collection, configs):
-        if os.path.isfile(identifier):
-            if configs.use_absolute_path:
-                output_path = os.path.abspath(identifier)
-            else:
-                output_path = os.path.relpath(identifier, configs.project_root)
-            full_result = {"path": output_path}
-            with open(identifier) as fin:
-                document = fin.read()
-                full_result["document"] = document
+    assert configs.project_root
 
-            structured_result.append(
-                {str(key): full_result[str(key)] for key in configs.include}
-            )
-        elif QueryInclude.chunk in configs.include:
-            chunks: GetResult = await collection.get(
-                identifier, include=[IncludeEnum.metadatas, IncludeEnum.documents]
-            )
-            meta = chunks.get(
-                "metadatas",
-            )
-            if meta is not None and len(meta) != 0:
-                chunk_texts = chunks.get("documents")
-                assert chunk_texts is not None, (
-                    "QueryResult does not contain `documents`!"
-                )
-                full_result: dict[str, str | int] = {
-                    "chunk": str(chunk_texts[0]),
-                    "chunk_id": identifier,
-                }
-                if meta[0].get("start") is not None and meta[0].get("end") is not None:
-                    path = str(meta[0].get("path"))
-                    with open(path) as fin:
-                        start: int = int(meta[0]["start"])
-                        end: int = int(meta[0]["end"])
-                        full_result["chunk"] = "".join(fin.readlines()[start : end + 1])
-                    full_result["start_line"] = start
-                    full_result["end_line"] = end
-                    if QueryInclude.path in configs.include:
-                        full_result["path"] = str(
-                            meta[0]["path"]
-                            if configs.use_absolute_path
-                            else os.path.relpath(
-                                str(meta[0]["path"]), str(configs.project_root)
-                            )
-                        )
-
-                    structured_result.append(full_result)
-            else:  # pragma: nocover
-                logger.error(
-                    "This collection doesn't support chunk-mode output because it lacks the necessary metadata. Please re-vectorise it.",
-                )
+    def make_output_path(path: str, absolute: bool) -> str:
+        if absolute:
+            if os.path.isabs(path):
+                return path
+            return os.path.abspath(os.path.join(str(configs.project_root), path))
+        else:
+            rel_path = os.path.relpath(path, configs.project_root)
+            if isinstance(rel_path, bytes):  # pragma: nocover
+                # for some reasons, some python versions report that `os.path.relpath` returns a string.
+                rel_path = rel_path.decode()
+            return rel_path
 
+    structured_result = []
+    for res in await get_query_result_files(collection, configs):
+        if isinstance(res, str):
+            output_path = make_output_path(res, configs.use_absolute_path)
+            io_path = make_output_path(res, True)
+            if not os.path.isfile(io_path):
+                logger.warning(f"{io_path} is no longer a valid file.")
+                continue
+            with open(io_path) as fin:
+                structured_result.append({"path": output_path, "document": fin.read()})
         else:
-            logger.warning(
-                f"{identifier} is no longer a valid file! Please re-run vectorcode vectorise to refresh the database.",
+            res = cast(Chunk, res)
+            assert res.path, f"{res} has no `path` attribute."
+            structured_result.append(
+                {
+                    "path": make_output_path(res.path, configs.use_absolute_path)
+                    if res.path is not None
+                    else None,
+                    "chunk": res.text,
+                    "start_line": res.start.row if res.start is not None else None,
+                    "end_line": res.end.row if res.end is not None else None,
+                    "chunk_id": res.id,
+                }
             )
     for result in structured_result:
         if result.get("path") is not None: